It’s an introductory lecture of the buzziest domain technology nowadays.The domain encapsulates a lot of new concepts, keywords, theories which make the full academic rainbow from computer science to business departments very busy to digest these upcoming, fast pacing concepts.Academies should, and do, offer new tracks to support these developments

This trivial equation tells the whole story.The subject of this lecture is comprised of two parts: Big Data &amp; Data ScienceAnd the lecture will appropriately be divided into these two parts.Of course we’ll see how they are connected and related to each other

The Big Data tour will be divided into 3 parts (as everything is in…big data, and you’ll see shortly)

The Big Data tour will be divided into 3 parts (as everything is in…big data, and you’ll see shortly)

We’ll start with the why and then the what will be better understood.Big Data is a business / technological aspect of a wider social phenomena we’re currently leave in.As all past social revolutions, they were all started with a technological revolution, e.g. the French revolution was a side effect of the industrial revolution.This is a same case where the Internet created a social revolutionEveryone is connected to everyone

Actually the Big Data as a phenomena started with the rise of Web2.0, where unlike the older Web 1.o, where only site owners created the online data, then came the users which create the content

The Big Data tour will be divided into 3 parts (as everything is in…big data, and you’ll see shortly)

Big Data -&gt; big numbers.Taken from http://visual.ly/what-big-data

Big Users is an equally big trend driving developers to use NoSQL databases.Most new applications are made available over the internet so people can easily access them.This has caused the number of simultaneous users for many applications to explode.The number of people connected to the internet is more than 2B and growing rapidly.The number of hours that the average user spends on the internet is growing too further increasing the number of simultaneous users.And, with the proliferation of smart phones, people use their applications more and more frequently further increasing the number of simultaneous users.All these simultaneous users leads to a rapidly growing number of database operations and the need for a far easier way to scale your database to meet these demands.Taken from Couchbase deck @ IGTCloud summit 2013http://www.go-gulf.com/blog/online-timehttp://business.time.com/2012/02/14/one-billion-smartphones-by-2016-here-comes-the-mobile-arms-race/

To summarize, the technology implications of the Big Data, Big User, and Cloud Computing mega trends are causing people to seriously rethink what database they use for their applications and are increasingly coming to the conclusion that NoSQL databases are a better fit than relational databases.

Finally, the move to cloud computing and SaaS business models is also driving developers to consider NoSQL databases.15 years ago most applications were developed with a client/server architecture and a packaged software business model that supported the needs of users on a company-by-company basis.Today, applications are increasingly developed using a 3-tier internet architecture, are cloud-based, and use a Software-as-a-Service business model that needs to support the collective needs of thousandsvof customersThis approach increasingly requires a horizontally scalable architecture that easily scales with the number of users and amount of data your application has.

The Big Data tour will be divided into 3 parts (as everything is in…big data, and you’ll see shortly)

This trivial equation tells the whole story.The subject of this lecture is comprised of two parts: Big Data &amp; Data ScienceAnd the lecture will appropriately be divided into these two parts.Of course we’ll see how they are connected and related to each other

Ok, we have the big data. Now, what are we doing with it?Big data is important if you want to be successful in analytic processing. But, why is that important? The answer is that success in a highly competitive, fast-moving marketplace is determined by who can capitalize on business opportunities before everyone else seizes the same opportunity. In this section we’ll meet the data scientists / data miners that coax treasures out of the huge volume of data

Although Onavo has started from a service that optimizes devices &amp; apps performance, on the way they’ve collected logs from these apps &amp; devices and became one of the leading mobile analytics aggregators in the world

Notations first.It has many names that mean more or less the same: the art of inference insights from data

Learning is comprised of three steps: First, we build our probabilistic model of the real worldThen, we train the model with labeled (supervised) examples, i.e. this is a car, this is not a car. This takes place offline.Last, online, we feed the model with a totally new example and expect it will predict for us the correct prediction

35.
Big Data - Summary
 BIG business opportunities
 The 3 V’s: Volume, Variety, Velocity
 Computing and DB paradigm shifts
 Flood of new (open source) technologies
 It’s definitely not just a buzz
It’s a real response to the world hectic paced evolution
 reducing costs by order of magnitude

 Still it doesn’t mean every business today will / should
transform its technology stack to support big data